private static void CreateClusters(IDataset data, string target, int contours, out IClusteringModel contourCluster, out Dictionary <int, string> contourNames, out double[][] borders)
{
var cpd = new ClusteringProblemData((Dataset)data, new[] { target });
contourCluster = KMeansClustering.CreateKMeansSolution(cpd, contours, 3).Model;
borders = Enumerable.Range(0, contours).Select(x => new[] { double.MaxValue, double.MinValue }).ToArray();
var clusters = contourCluster.GetClusterValues(cpd.Dataset, cpd.AllIndices).ToArray();
var targetvalues = cpd.Dataset.GetDoubleValues(target).ToArray();
foreach (var i in cpd.AllIndices)
{
var cl = clusters[i] - 1;
var clv = targetvalues[i];
if (borders[cl][0] > clv)
{
borders[cl][0] = clv;
}
if (borders[cl][1] < clv)
{
borders[cl][1] = clv;
}
}
contourNames = new Dictionary <int, string>();
for (var i = 0; i < contours; i++)
{
contourNames.Add(i, "[" + borders[i][0] + ";" + borders[i][1] + "]");
}
}
private KMeansClustering(KMeansClustering original, Cloner cloner)
: base(original, cloner)
{
}
